Businesses worldwide are discovering the power of new big data processing and analytics frameworks like Apache Hadoop and Apache Spark, but they are also discovering some of the challenges of operating these technologies in on-premises data lake environments. They may also have concerns about the future of their current distribution vendor. To address this, AWS is making it easier than ever to migrate to Amazon EMR. This fireside chat will be your comprehensive guide to offer sound business and technical advice on how to move from on-premises big data deployments to EMR.
In this episode, we'll talk about:
- Why to migrate big data to AWS (including Apache Spark and Hadoop)
- The decisions involved in planning for a migration to Amazon EMR
- The ROI and TCO impact from others who have moved
Who should attend?
CIO, CTO, Data Engineer, Data Architect, Data Scientist, Head of Analytics, Data Warehouse & Database teams
GM, Databases, Analytics, and Blockchain, AWS
Rahul Pathak is currently General Manager of Databases, Analytics, and Blockchain at AWS. He owns Amazon Managed Blockchain, Athena, EMR, DocumentDB, Neptune, and Timestream at AWS. During his 7+ years at AWS, Rahul has focused on managed database and analytics services. Rahul has over twenty years of experience in technology and has co-founded two companies, one focused on digital media analytics and the other on IP-geolocation. He holds a degree in Computer Science from MIT and an Executive MBA from the University of Washington.
Principal Product Manager - Amazon Athena and Amazon EMR, AWS
Abhishek is a seven-year veteran of AWS, and he remains obsessed with helping customers find success around big data and data lakes. He is a graduate of Dharmsinh Desai University with a degree in computer engineering.